Biomarkers of fast progression of chronic kidney disease

ABSTRACT

The present invention relates to methods for the prediction of the progression of chronic kidney disease in a patient. More particularly, the invention relates to the early prediction of the fast progression of chronic kidney disease using specific biomarker signatures in urine sample of patients.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No. 15/604,888, filed May 25, 2017, which is a continuation of International Patent Application No. PCT/EP2015/077504, having an international filing date of Nov. 24, 2015, the entire contents of which are incorporated herein by reference, and which claims benefit under 35 U.S.C. § 119 to European Patent Application No. 14306879.9, filed on Nov. 25, 2014.

SEQUENCE LISTING

The instant application contains a Sequence Listing which has been submitted electronically in ASCII format and is hereby incorporated by reference in its entirety. Said ASCII copy, created on Aug. 20, 2019, is named P32446_US_1_SequenceListing.txt and is 66,028 bytes in size.

FIELD OF THE INVENTION

The present invention relates to methods for the prediction of the progression of chronic kidney disease in a patient. More particularly, the invention relates to the early prediction of the fast progression of chronic kidney disease using specific biomarker signatures in urine sample of patients.

BACKGROUND OF THE INVENTION

Chronic Kidney Disease (CKD) currently affects about 10% of the Western population and the incidence is thought to be increasing worldwide. Low glomerular filtration rate (GFR) is associated with increased risk of death from cardiovascular as well as age standardized all-cause mortality.^(1,2) This increased risk of death arises even prior to patients reaching end stage renal disease (ESRD)¹. CKD is thus a significant public health problem. Some patients with CKD will progress rapidly on to end stage renal disease (ESRD) and are therefore at higher risk, while in others the CKD may remain stable or even improve.³ Identifying those patients likely to progress is paramount to stratification. Albuminuria is a good predictor of CKD progression.⁴ Albuminuria can however regress in spite of on-going CKD.⁵

There are a number of effectors which may play a role in CKD progression and could be potential candidates as biomarker of kidney progression.

Transforming growth factor-β (TGF-β)⁶ pathway and its downstream effector connective tissue growth factor (CTGF)^(2,8) are known major driving factors of matrix synthesis and potential factor of fibrosis development. Extracellular matrix (ECM) accumulation, which is the building block of fibrosis, consists of molecules such as collagen III so we studied Procollagen III amino terminal propeptide (PIIINP)^(9,10) (an indirect index of the amount of collagen is synthesised) as well as fibronectin 1 (FN1)¹¹ and periostin¹²—two other significant ECM molecules. As their names imply, other molecules involved in ECM remodelling for study are matrix metalloprotease 9 (MMP9) and tissue inhibitor of metalloprotease 1 (TIMP1) which respectively breakdown ECM and inhibit this break down action¹³.

Inflammatory processes are thought to play important roles in driving eventual fibrosis. From an inflammation point of view, a number of inflammatory chemokines and cytokines may be involved: monocyte chemoattractant protein 1 (MCP1)¹⁴, osteopontin¹⁵, Interleukin 18 (IL18)¹⁶, IL6¹⁷, and leukocyte inhibitory factor (LIF)¹⁸ and growth and differntiaiton factor 15 (GDF15)¹⁹. Growth factors such as ligands of the epidermal growth factor (EGF) receptor (EGFR) have a role in cell growth and proliferation. Ligands such as EGF and transforming growth factor alpha (TGF-α) in the pathophysiology may have a role in CKD²⁰. Neutrophil gelatinase associated lipocalin can amongst its many other roles act downstream of the EGFR²¹. The vascular endothelial growth factors A (VEGFA)²² and C (VEGFC)²³ are growth factors implicated in angiogenesis and lymphangiogenesis both of which also have identified roles in CKD progression.

Other molecules are more specific to the kidney structure and function and are expressed at varying levels depending on the degree of kidney damage. Fatty Acid Binding Protein 1 (FABP1)²⁴⁻²⁶ is mainly expressed in the proximal tubule. Kidney injury molecule 1 (KIM1) is also expressed on the renal tubules but exclusively in disease state.²⁷ EGF is also expressed in the distal tubules²⁸. Cystatin C is a 13.4 kDa cysteine protease inhibitor, freely filtered in the glomerulus and reabsorbed in the tubules²⁹ and thus elevated urinary levels might suggest tubular damage. Uromodulin is expressed in the loop of Henle and is secreted into the urine³⁰.

Despite this physiopathological information, there is no reliable biomarker signature for use in prediction of fast progression.

In a large study by Tangri et al. involving a total of 8391 patients in CKD stages 3 to 5, a predictive model for CKD progression using routinely measured indices such as plasma albumin, calcium, phosphate, bicarbonate, albuminuria and taking account of age, gender and baseline estimated glomerular filgration rate (eGFR) was shown to be predictive of CKD progressing to renal failure33. The main outcome measure here was requirement for renal replacement therapy.

Other groups have looked at other models to predict CKD progression employing for the most part the less accurate eGFR rather the measured glomerular filtration rate (mGFR) gold standard. The eGFR is clearly easier and cheaper to measure but the fact that the findings are not completely reproduced using eGFR equation is not surprising. The inaccuracy of eGFR in the face of mGFR has already been reported due to the lack of sensitivity of the eGFR43. The error is further amplified when looking at progression where two or more GFRs have to be taken into account.

The detection of albuminuria together with the demographic risk factors remain however the standard approach. Increasing prediction accuracy by adding in reliable biomarkers, particularly urinary biomarkers, would thus be useful. In particular, there is an increasing need in the art for an in vitro early detection method of patients at risk of fast progression of chronic kidney disease.

The present invention thus fulfills this need as disclosing specific combinations of urinary biomarkers associated to CKD progression and providing novel prediction methods of CKD progression and their kits.

SUMMARY OF THE INVENTION

The present invention relates to an in vitro method for the prediction of fast progression of chronic kidney disease in a subject, comprising the steps of evaluating the expression of one or more biomarkers in a biological sample obtained from said subject, wherein said one or more biomarkers are selected from the group consisting of transforming growth factor alpha (TGF-α), epidermal growth factor (EGF), and monocyte chemoattractant protein 1 (MCP1).

In specific embodiments, the prediction method of the invention comprises evaluating at least the expressions of EGF, MCP1, and TGF-α, and, optionally, Neutrophil Gelatinase-associated Lipocalin (NGAL).

In other specific embodiments that may be combined with the previous embodiments, the evaluating step include (a) quantifying the expression of one or more of the selected biomarkers in a biological sample obtained from said subject to obtain an expression value for each quantified biomarker, and (b) comparing said expression value obtained at step (a) to a corresponding control value, wherein an expression value of EGF below a control value and/or an expression value of MCP1 above a control value and/or an expression value of TGF-α above a control value, and/or an expression value of NGAL above a control value, indicates that the human subject is at increased risk of fast progression.

In other specific embodiments that may be combined with the previous embodiments, the expression of one or more biomarkers selected from the group consisting of: Growth and Differentiation Factor 15 (GDF15), Neutrophil Gelatinase-associated Lipocalin (NGAL), Cystatin C, Fatty Acid Binding Protein (FABP), Fibronectin, Kidney Injury Molecule 1 (KIM1), Osteopontin, Tissue Inhibitor of Mettaloprotease 1 (TIMP1), Uromodulin and Vascular Endothelial Growth Factor A (VEGFA), Interleukin-6 (IL6), Leukemia Inhibitory Factor (LIF), Matrix Metallopeptidase 9 (MMP9) is further evaluated.

In other specific embodiments that may be combined with the previous embodiments, said biological sample for use in the method is urine or serum sample.

In other specific embodiments that may be combined with the previous embodiments, protein expression of each biomarker is evaluated in the biological sample, for example as quantified in an immunoassay.

In other specific embodiments that may be combined with the previous embodiments, said subject predicted of fast progression is then selected for treatment with a therapeutic agent for treating chronic kidney disease.

The invention further relates to an in vitro method for monitoring the efficacy of a therapeutic agent for treating chronic kidney disease in a subject, comprising evaluating the expressions of biomarkers in a biological sample of said subject, wherein said biomarkers are EGF, MCP1, and TGF-α and, optionally, evaluating the expression of NGAL.

In other specific embodiments that may be combined with the previous embodiment, a first evaluating step prior to treating chronic kidney disease is carried out and is then repeated during or after said treatment step, wherein a change in the expressions of said biomarkers is indicative of a response to said treatment.

The invention further relates to a kit for carrying out any one of the above methods of the invention, said kit comprising means for quantifying protein expression of at least the following biomarker: EGF, MCP1, and TGF-α, and, optionally, means for quantifying protein expression of NGAL.

In other specific embodiments that may be combined with the previous embodiments, said means for quantifying protein expression include unlabelled antibodies specific of each biomarker and, optionaly, second labeled antibodies for detecting said biomarker/unlabelled antibodies in an immunoassay.

For example, in a more specific embodiment, the kits of the invention, for use in an immunoassay, comprises:

(i) antibodies specific of EGF;

(ii) antibodies specific of MCP1; and,

(iii) antibodies specific of TGF-α,

(iv) optionally antibodies specific of NGAL.

In specific embodiments, said antibodies comprised in the kit are immobilized on a support. In other specific embodiments, said antibodies comprised in the kit are conjugated to reporter molecule(s).

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1: Best model combining urinary biomarkers to predict ‘fast progressor’ status in CID progression. (a): Forest plot showing odds ratios, OR (95% Confidence Interval, CI) of Epidermal Growth Factor (EGF), Monocyte Chemoattractant Protein (MCP1), Transforming Growth Factor-α (TGF-α) and albuminuria in the model; (b): Receiver Operating Characteristic (ROC) curves comparing the area under the curve (AUC) of the best model with the three BMs Epidermal Growth Factor (EGF), Monocyte Chemoattractant Protein (MCP1), Transforming Growth Factor-α (TGF-α) and albuminuria to the AUC of the model without the biomarkers (BMs) but albuminuria alone. Models were adjusted for albuminuria, recruitment centre, history of diabetes and mean mGFR.

FIG. 2: Percent change in glomerular filtration rate (GFR) per year according to measured GFR (mGFR) and estimated GFR *(eGFR).

Squares represent patients in whom there is concordance in rates of progression using either method: filled squares are fast progressors (n=38) and empty squares are slow progressors (n=131). The circles represent patients in whom there is disconcordance in the two methods: filled circles are fast progressors by mGFR but not eGFR (n=30) and empty circles are fast progressors by eGFR but not mGFR (n=30). The grey line shows the line of regression (pearson's correlation coefficient r=0.5). 5 outliers were not represented here (>50 and <−50% change in baseline mGFR/year). *eGFR was estimated using the modification of diet in renal disease (MDRD) equation.

FIG. 3: Biomarker Tertiles of biomarkers in the signature.

Percentage of fast progressors (>10% loss in measured glomerular filtration rate (mGFR) per year) in each of the tertiles of urinary biomarkers in Model 5. Albumin tertiles T1: <3.58; T2: 3.58-33.02; and T3: ≥33.02 mg/mmol. Epidermal growth factor (EGF) tertiles T1: <0.46; T2: 0.46-0.81; and T3: ≥0.81 μg/mmol; monocyte chemoattractant protein (MCP1) tertiles T1: <17.7; T2: 17.7-35.6; and T3: ≥32.6 ng/mmol; neutrophil gelatinase associated lipocalin (NGAL) tertiles T1: <0.26; T2: 0.26-0.76; and T3: ≥0.76 μg/mmol; and transforming growth factor-α (TGF-α) tertiles T1: <0.30; T2: 0.30-0.47; and T3: ≥0.47 ng/mmol. A J-shaped distribution of tertiles can be observed for NGAL

DETAILED DESCRIPTION OF THE INVENTION

Methods allowing an early prediction of the likelihood of fast progression of chronic kidney disease in patients are provided by the present invention.

Particularly, it is provided herein prognostic methods and kits allowing prediction of the fast progression of CKD and thus risk of progression to end stage renal disease in patients.

According to the present invention, highly reliable sets of urinary biological markers that are indicative of an increased risk of fast progression of chronic kidney disease have been identified.

Thus, an object of the present invention consists of an in vitro method for the prediction of fast progression of chronic kidney disease in a subject, comprising the steps of evaluating the expression of one or more biomarkers in a biological sample obtained from said subject, wherein said one or more biomarkers are selected from the group consisting of epidermal growth factor (EGF), monocyte chemoattractant protein 1 (MCP1), transforming growth factor alpha (TGF-α), Growth and Differentiation Factor 15 (GDF15), neutrophil gelatinase-associated lipocalin (NGAL), Cystatin C, Fatty Acid Binding Protein (FABP), Fibronectin, Kidney Injury Molecule 1 (KIM1), Osteopontin, Tissue Inhibitor of Mettaloprotease 1 (TIMP1), Uromodulin Interleukin-6 (IL6), Leukemia Inhibitory Factor (LIF), Matrix Metallopeptidase 9 (MMP9) and Vascular Endothelial Growth Factor A (VEGFA).

In specific embodiments, the expression of 2, 3, 4 or 5 of said biomarkers listed above is evaluated.

Said one or more biomarkers of the prediction method according to the invention are more particularly selected from the group consisting of EGF, MCP1 and TGF-α.

In specific embodiments, the prediction method of the invention comprises evaluating at least the expressions of EGF, MCP1 and TGF-α for example, their protein expression, and optionally, of NGAL.

As it is shown in the examples below, when comparing the expression values of candidate biomarkers in urine samples between slow progressor and fast progressor of CKD, the inventors have identified specific biomarkers and their combinations with statistically different expression in fast progressor vs slow progressor or healthy subjects, such biomarkers are called hereafter the “predictive biomarkers” and listed in Table 1 below.

Certain Definitions

The term “patient” or “subject” which is used herein interchangeably refers to a human being, including for example a man or a woman that has or is suspected to have a chronic kidney disease or is at an early stage of CKD, or a subject at risk in developing chronic kidney disease considering the demographic risk factors of CKD.

The term “chronic kidney disease” or “CKD” is used herein interchangeably to refer to a condition defined as abnormalities of kidney structure or function, present for more than 3 months, with implications for health which can occur abruptly, and either resolve or become chronic (Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease Guidelines (KDIGO 2012). CKD is a general term for heterogeneous disorders affecting kidney structure and function with variable clinical presentation, in part related to cause, severity and the rate of progression (Kidney International Supplements (2013) 3, vii). In particular, definition and identification of CKD may be defined with the following criteria:

1. For individuals at higher risk of progression, and/or where measurement will impact therapeutic decisions 2. Recognize that small fluctuations in GFR are common and are not necessarily indicative of progression. 3. Define CKD progression based on one of more of the following (Not Graded): a. Decline in GFR category (Z90 [G1], 60-89 [G2], 45-59 [G3a], 30-44 [G3b], 15-29 [G4], 015 [G5] ml/min/1.73 m2). A certain drop in eGFR is defined as a drop in GFR category accompanied by a 50% or greater drop in eGFR from baseline or End-Stage Renal Disease (ESRD, eGFR<15 ml/min/1.73 m2, Renal Replacement Therapy or death or composite of the above parameters. b. Rapid progression is defined as a sustained decline in eGFR of more than −3.3% per year. c. The confidence in assessing progression is increased with increasing number of serum creatinine measurements and duration of follow-up

The term “biological sample” is intended to refer to biological fluids and isolates thereof isolated from a subject. It can include without limitation, blood sample, e.g., whole blood, plasma and serum sample, saliva or urine sample. In a particular embodiment, a biological sample is urine sample.

The term “prognosis” is used herein to refer to the prediction of the outcome of the patient as to whether their GFR falls at fast or slow rate.

As used herein, the term “prediction of fast progression” does not necessarily consist of an absolute response. It may allow to determine the probability (risk) of fast progression of chronic kidney diseasein a subject, or, it may consist of a response allowing to determine an increased risk of fast progression in a subject compared to the average risk of fast progression of CKD in a population, rather than giving a precise probability for the risk.

In other words, a patient who is predicted to be a fast progressor according to the methods of the invention is a subject at increased risk of being a fast progressor. In certain embodiments, the prediction is expressed as a statistical value, including a P value, as calculated from the expression values obtained for each of the one or more biomarkers that have been evaluated.

The term “fast progression” particularly refers to an evolution of CKD as measured by the loss of more than 10% of the baseline measured glomerular filtration rate (mGFR) per year in a subject suffering from CKD also called herein “fast progressor”. Fast progressors are more likely to progress onto end stage renal disease (ESRD), which stage is associated with a significant morbidity and mortality risk. Accordingly, in a specific embodiment of the methods of the invention, a subject predicted of fast progression, is a subject predicted to lose more than 10% of the baseline measured glomerular filtration rate (mGFR) per year, for example as measured from an initial measurement until a subsequent time around a year later. On the contrary, a “slow progressor” is a subject that is losing less than 10% of the baseline measured glomerular filtration rate (mGFR) per year.

As used herein, the term “early prediction” refers to a prediction carried out in a subject at an early stage of CKD, for example at stage 1 or 2, with GFR >60 ml/mn/1.73 m2 33, stage 3 or which even who has not yet been diagnosed as having CKD, for example according to albuminuria, proteinuria or creatinine concentration conventional diagnostic methods. The prognosis methods of the invention are particularly appropriate for early prediction of fast progression of CKD in a subject.

As used herein, the term “biological marker” or “biomarker” refers to an indicator of e.g. a pathological state of a patient, which can be detected in a biological sample of the patient. Biomarkers, include, but are not limited to, DNA, RNA, protein, carbohydrate, or glycolipid-based molecular markers.

The term “protein” is used interchangeably with the term “polypeptide” and in its broadest sense refers to a compound of two or more subunit amino acids. The subunits can be linked by peptide bonds.

The term “kit” as used herein refers to a collection of the aforementioned components which may be provided separately or within a single container. The container also comprises instructions for carrying out the method of the present disclosure. These instructions may be in the form of a manual or may be provided by a computer program code which is capable of carrying out the comparisons referred to in the methods of the present disclosure and to establish a diagnosis accordingly when implemented on a computer or a data processing device. The computer program code may be provided on a data storage medium or device such as an optical storage medium (e.g., a Compact Disc) or directly on a computer or data processing device.

The Predictive Biomarkers for Use in the Methods of the Invention

The predictive biomarkers are described hereafter by their acronyms, full name and UniprotKB/Swissprote nomenclature and SEQ ID NOs.

TABLE 1 List of predictive biomarkers UniprotKB/ SEQ Swiss- ID Biomarker Full name Prot¹ NO: EGF Epidermal Growth Factor P01133 1 GDF15 Growth and Differentiation Factor 15 Q99988 2 TGF-α Transforming Growth Factor alpha P01135 3 MCP1 Monocyte Chemoattractant Protein 1 P13500 4 FABP1 Fatty Acid Binding Protein P07148 5 Cystatin C Cystatin C P01034 6 Fibronectin Fibronectin P02751 7 KIM1 Kidney Injury Molecule 1 Q96D42 8 NGAL Neutrophil Gelatinase Associated P80188 9 Lipocalin TIMP1 Tissue Inhibitor of Metalloprotease 1 P01033 10 Uromodulin Uromodulin P07911 11 VEGF-A Vascular Endothelial Growth Factor P15692 12 A Osteopontin Osteopontin P10451 13 IL6 Interleukin 6 P05231 14 LIF Leukemia Inhibitory Factor P15018 15 MMP9 Matrix Metallopeptidase 9 (MMP9) P14780 16 ¹http://www.uniprot.org/

In the present invention, when referring to the biomarkers, it particularly refers to the protein of said biomarker and/or its post-translational modifications.

The protein sequences of the corresponding biomarkers can be found at http.//www.uniprot.org according to the corresponding references as shown in Table 1, or SEQ ID NOs 1-16. Of course, any natural variations of said protein sequences are included in the definition of said biomarkers for use in the present invention.

Quantifying the Expression of a Predictive Biomarker

The prediction method of the invention comprises a step of evaluating the expression of one or more of the predictive biomarkers in a biological sample.

As used herein, the term “evaluating” typically include the steps of (a) quantifying the expression of each of the selected predictive biomarkers in a biological sample obtained from said subject to obtain expression values, and (b) comparing the obtained expression values of said predictive biomarkers to corresponding control values, wherein differences in the expression values compared to the respective control values is indicative that the subject is at increased risk of fast progression.

Expression of the biomarkers can be quantified by determining gene or protein expression of the predictive biomarkers in the biological sample of a subject, for example serum or urine sample. The quantification may be relative (by comparing the amount of a biomarker to a control with known amount of biomarker for example and detecting “higher” or “lower” amount compared to that control) or more precise, at least to determine the specific amount relative to a known control amount.

In one specific embodiment, the expression of the biomarkers can be quantified by examining protein expression of at least one or more of the predictive biomarkers in the biological sample, for example urine sample, of a subject. In specific embodiments, the protein expressions of at least EGF, MCP1 and TGF-α and optionally, NGAL are quantified in the biological sample of a subject, for example urine sample.

Various methods are known in the art for detecting protein expression levels in such biological samples, including various immunoassays methods. They include but are not limited to radioimmunoassays, ELISA (enzyme linked immunosorbent assays), “sandwich” immunoassays, immunoradiometric assays, in situ immunoassays (using e.g., colloidal gold, enzyme or radioisotope labels), western blot analysis, immunoprecipitation assays, immunofluorescent assays, flow cytometry, immunohistochemistry, confocal microscopy, enzymatic assays, surface plasmon resonance and PAGE-SDS.

Determining the protein level involves for example measuring the amount of any immunospecific binding that occurs between an antibody that selectively recognizes and binds to the polypeptide of the biomarker in a sample obtained from a patient. These assays may also include direct binding of labelled antibody to a target biomarker.

Sandwich assays are among the most useful and commonly used assays. A number of variations of the sandwich assay technique exist, and all are intended to be encompassed by the present invention. Briefly, in a typical forward assay, an unlabeled antibody is immobilized on a solid substrate, and the sample to be tested brought into contact with the bound molecule. After a suitable period of incubation, for a period of time sufficient to allow formation of an antibody-antigen complex, a second antibody specific to the antigen, but labeled with a reporter molecule capable of producing a detectable signal is then added and incubated, allowing time sufficient for the formation of another complex of antibody-antigen-labeled antibody. Any unreacted material is washed away, and the presence of the antigen is determined by observation of a signal produced by the reporter molecule. The results may either be qualitative, by simple observation of the visible signal, or may be quantitated by comparing with a control sample containing known amounts of biomarker.

Variations on the forward assay include a simultaneous assay, in which both sample and labeled antibody are added simultaneously to the bound antibody. These techniques are well known to those skilled in the art, including any minor variations as will be readily apparent. In a typical forward sandwich assay, a first antibody having specificity for the biomarker is either covalently or passively bound to a solid surface.

The binding processes are well-known in the art and generally consist of cross-linking covalently binding or physically adsorbing, the polymer-antibody complex is washed in preparation for the test sample. An aliquot of the sample to be tested is then added to the solid phase complex and incubated for a period of time sufficient (e.g. 2-40 minutes or overnight if more convenient) and under suitable conditions (e.g. from room temperature to 40[deg.]C such as between 25[deg.] C and 32[deg.] C inclusive) to allow binding of any subunit present in the antibody. Following the incubation period, the antibody subunit solid phase is washed and dried and incubated with a second antibody specific for a portion of the biomarker. The second antibody is linked to a reporter molecule which is used to indicate the binding of the second antibody to the molecular marker.

An alternative method involves immobilizing the predictive biomarkers in the sample and then exposing the immobilized biomarkers to specific antibody which may or may not be labeled with a reporter molecule. Depending on the amount of biomarker target and the strength of the reporter molecule signal, a bound biomarker target may be detectable by direct labelling with the antibody. Alternatively, a second labeled antibody, specific to the first antibody is exposed to the target-first antibody complex to form a target-first antibody-second antibody tertiary complex. The complex is detected by the signal emitted by the reporter molecule.

By “reporter molecule”, as used in the present specification, is meant a molecule which, by its chemical nature, provides an analytically identifiable signal which allows the detection of antigen-bound antibody. The most commonly used reporter molecules in this type of assay are either enzymes, fluorophores or radionuclide containing molecules (i.e. radioisotopes) and chemiluminescent molecules.

In the case of an enzyme immunoassay or ELISA assay, an enzyme may typically be conjugated to the second antibody, generally by means of glutaraldehyde or periodate. As will be readily recognized, however, a wide variety of different conjugation techniques exist, which are readily available to the skilled artisan. Commonly used enzymes include horseradish peroxidase, glucose oxidase, -galactosidase and alkaline phosphatase, amongst others. The substrates to be used with the specific enzymes are generally chosen for the production, upon hydrolysis by the corresponding enzyme, of a detectable color change. Examples of suitable enzymes include alkaline phosphatase and peroxidase. It is also possible to employ fluorogenic substrates, which yield a fluorescent product rather than the chromogenic substrates noted above. In all cases, the enzyme-labeled antibody is added to the first antibody-molecular marker complex, allowed to bind, and then the excess reagent is washed away. A solution containing the appropriate substrate is then added to the complex of antibody-antigen-antibody. The substrate will react with the enzyme linked to the second antibody, giving a qualitative visual signal, which may be further quantitated, usually spectrophotometrically, to give an indication of the amount of biomarker which was present in the sample.

Alternately, fluorescent compounds, such as fluorescein and rhodamine, may be chemically coupled to antibodies without altering their binding capacity. When activated by illumination with light of a particular wavelength, the fluorochrome-labeled antibody adsorbs the light energy, inducing a state to excitability in the molecule, followed by emission of the light at a characteristic color visually detectable with a light microscope. As in the EIA, the fluorescent labeled antibody is allowed to bind to the first antibody-molecular marker complex. After washing off the unbound reagent, the remaining tertiary complex is then exposed to the light of the appropriate wavelength, the fluorescence observed indicates the presence of the molecular marker of interest. Immunofluorescence and EIA techniques are both very well established in the art. However, other reporter molecules, such as radioisotope, chemiluminescent or bioluminescent molecules, may also be employed.

In specific embodiment of the prediction method of the invention, the quantifying step thus allows to obtain an “expression value” for each biomarker tested in the biological sample, for use in the comparing step.

For ease of use in the comparing step, said expression value may consist of a normalized (relative) value which is obtained after comparison of the absolute expression level value with a reference value, said reference value consisting for example of the expression level value of reference proteins in the biological sample.

For example, in a specific embodiment, creatinine level may be used for adjusting the expression values to normalized expression values. Usually, creatinine can be determined by enzymatic or colorimetric test systems in urine.

Comparing Expression Value of Each Biomarker to Corresponding Control Values

The methods of the invention is based on quantifying the expression of one or more predictive biomarkers in a biological sample of a subject, as described above, and comparing each expression value of said biomarkers to corresponding control values.

The term “comparing” as used herein refers to a comparison of corresponding parameters or values, e.g. an absolute level value is compared to an absolute control level value which a concentration is compared to a control concentration and normalised value is compared to corresponding control normalised value.

As used herein, the term “control values” refers to expression values of the biomarkers in control subject or a group of control subjects, which allows assessing whether an individual is predicted of CKD fast progression.

According to some embodiments, the control value is determined based on biomarker expression from a control subject or a group of control subjects which has been characterized as fast progressor, slow progressor or as healthy subject.

The control value applicable for a specific subject may vary depending on various physiological parameters such as age, gender, or subpopulation, as well as on the test format, the sample and the ligand used for the quantification of the biomarker referred to herein. These factors and ways to take them into account when determining the control values are generally known in the field. In some embodiments, control values can be calculated for a cohort of subjects as specified in the Examples, based on the average or mean or median values for a biomarker by applying standard statistical methods.

Accordingly a control value to be used for the aforementioned method of the present invention (i.e. a threshold which allows for identifying an individual at higher risk of fast progression) may be the mean/median expression value (or normalized (relative) mean/median value) of a biomarker protein expression as quantified in a group of control subjects.

Examples of such normalized median values for each predictive biomarkers in men and women are given in the Examples below at Table 3 (the values have been adjusted to normalized value with urine creatinine concentration).

Said control value can also be determined by routine experimentation depending on the quantification methods and the predictive biomarkers that will be used for the methods of the invention.

For example, said control value corresponds to the expression (optionally normalized) mean/median value observed for slow progressor group of patients, and a patient is predicted to be a fast progressor when the expression (optionally normalized) value is statistically different from the control value, for example increased as compared to the control value, or decreased as compared to a control value.

Alternatively, said control value corresponds to the expression level value observed for fast progressor patients, and a patient is predicted to be a fast progressor when the expression level value is statistically not different from the control value.

For example, in a specific prediction method of the invention, the evaluating step include (a) quantifying the expression of one or more of the selected biomarkers in a biological sample obtained from said subject to obtain an expression value for each biomarker, and (b) comparing said expression value of each biomarker obtained at step (a) to a corresponding control value, wherein an expression value of EGF below a control value and/or an expression value of MCP1 above a control value and/or an expression value of TGF-α above a control value, and/or an expression value of NGAL above a control value, indicates that the subject is at increased risk of fast progression of CKD.

The comparison referred to in step (b) of the methods of the invention may be carried out manually or computer assisted.

For a computer-assisted comparison, the expression values may be compared to control values which are stored in a database by a computer program. The computer program may further evaluate the result of the comparison, i.e. automatically provide the desired assessment in a suitable output format.

As it is shown in the examples and more specifically in Table 5 of the Examples below, a combination of 2, 3, 4, or 5 of the predictive biomarkers according to the invention can be statistically relevant for predicting fast progression of CKD for a patient.

Any combination of two, three, four, five or more of the predictive biomarkers of the invention is encompassed by the methods of the invention.

The comparing step may not necessarily include a separate comparison of the expression values of each biomarker with their corresponding control values. In specific embodiments, a multi-biomarker score value can be obtained by combining together the expression values or their normalized values and compared to a corresponding multibiomarker score control value.

In a particular embodiment, the expression values are obtained at least for the three following biomarkers in a patient: epidermal growth factor (EGF), monocyte chemoattractant protein 1 (MCP1), and transforming growth factor alpha (TGF-α) and, optionally, of NGAL.

To improve statistical prediction of the methods according to the invention, one can further include the albuminuria, proteinuria, plasma or serum creatinine concentration and/or demographic risk factors for chronic kidney disease progression.

As used herein, said demographic risk factors include age, body mass index, mean arterial pressure, gender, Black/African ethnicity, diabetes, history of cardiovascular diseases, smoker, satus, the non-use of angiotensin pathway blockade, history of urinary infections, haematuria, familial history of kidney disease.

In a specific embodiment, the invention relates to a method for the prediction of fast progression of chronic kidney disease in a subject, comprising evaluating at least the expressions of EGF, MCP1 and TGF-α, and, optionally, NGAL, and wherein albuminuria or proteinuria is further determined from said subject. Albuminuria or proteinuria may be determined from the same biological sample or in a different biological sample according to well known methods in the art.

In specific embodiments, the prediction methods include evaluating the expressions of at least epidermal growth factor (EGF), monocyte chemoattractant protein 1 (MCP1), and transforming growth factor alpha (TGF-α) and at least one or more of the following biomarkers: GDF15, FABP1, Cystatin C, Fibronectin, KIM1, NGAL, TIMP1, Uromodulin, Osteopontin, IL6, MMP9, LIF and VEGF-A.

In specific embodiments, the prediction methods include evaluating the expression in urine sample of the specific biomarker combination of EGF, TGF-α and MCP1.

In other specific embodiments, the prediction methods include evaluating the expression in urine sample of the specific biomarker combination of EGF, TGF-α, MCP1 and NGAL.

In other specific embodiments, the prediction methods include evaluating the expression in urine sample of the specific biomarker combination of EGF, TGF-α, MCP1 and NGAL.

In other specific embodiments, the prediction methods include evaluating the expression in urine sample of the specific biomarker combination of EGF, TGF-α, MCP1, and FABP1.

In other specific embodiments, the prediction methods include evaluating the expression in urine sample of the specific biomarker combination of EGF, TGF-α, MCP1 and Cystatin C.

In other specific embodiments, the prediction methods include evaluating the expression in urine sample of the specific biomarker combination of EGF, TGF-α, MCP1 and Fibronectin.

In other specific embodiments, the prediction methods include evaluating the expression in urine sample of the specific biomarker combination of EGF, TGF-α, MCP1 and KIM1.

In other specific embodiments, the prediction methods include evaluating the expression in urine sample of the specific biomarker combination of EGF, TGF-α and TIMP1.

In other specific embodiments combined with the above specific embodiments, the prediction methods also include evaluating albumin in urine sample, either together with the other predictive biomarkers (from the same biological sample) or separately (from different sample).

Assaying for Biomarker Expression and the Treatment for CKD

The invention further relates to patient stratification methods. In particular, patient identified at increased risk of fast progression of CKD may be selected according to the methods of the invention for a therapeutic treatment of CKD. Accordingly, the invention further includes a method comprising

(i) identifying whether a patient is at increased risk of fast progression of CKD according to the above defined prediction methods, and, (ii) treating said fast progressor patient identified at step (i) with a suitable therapeutic agent for treating CKD.

As used herein, the term “treating” or “treatment” refers to measures, wherein the object is to prevent or slow down (lessen) the targeted pathologic condition or disorder or slow down or relieve one or more of the symptoms of the disorder. In a specific embodiment, a subject is “successfully treated” for chronic kidney disease if, after receiving a therapeutic agent, the patient shows observable and/or measurable decrease or change from baseline in and/or measurable rate of change from baseline over time (e.g. over 3 months [12 weeks], or 6 months [24 weeks], or 9 months [36 weeks], or 12 months [1 year, 52 weeks] in one or more of the following: mGFR, percentage loss of mGFR in 1 year, eGFR, percentage of loss in eGFR.

Therapeutic agents for treating fast progressors of chronic kidney disease according to the above methods, may include without limitation (i) an angiotensin converting enzyme inhibitor (ACEi) such as, Captopril (Capoten), Zofenopril, Enalapril (Vasotec/Renitec), Ramipril (Altace/Prilace/Ramace/Ramiwin/Triatec/Tritace), Quinapril (Accupril), Perindopril (Coversyl/Aceon/Perindo), Li sinopril (Listril/Lopril/Novatec/Prinivil/Zestril), Benazepril (Lotensin), Imidapril (Tanatril), Trandolapril (Mavik/Odrik/Gopten), Cilazapril (Inhibace) and Phosphonate-containing agents such as Fosinopril (Fositen/Monopril) or (ii) an angiotensin receptor blocker (ARB) such as Losartan (Cozaar), Candesartan (atacand), valsartan (Diovan), Irbesartan (Avapro), Telmisartan (Micardis), Eprosartan (Teveten), Olmesartan (Benicar/Olmetes) and Azilsartan (edarbi).

Administration of a suitable therapeutic agent for treating chronic kidney disease to said patient can be effected in one dose, continuously or intermittently throughout the course of treatment.

Methods of determining the most effective means and dosage of administration of a treatment are well known to those of skill in the art and will vary with the composition used for therapy, the purpose of the therapy, and the subject being treated. Single or multiple administrations can be carried out with the dose level and pattern being selected by the treating physician. Suitable dosage formulations and methods of administering the agents may be empirically adjusted.

If a patient is predicted to be a slow progressor, alternative therapies may be preferred. Prior to administering suitable therapeutic agents, further diagnosis methods may be applied to further validate the risk of fast progression in said patient.

Another aspect of the invention relates to an in vitro method for monitoring the efficacy of a treatment for treating chronic kidney disease in a patient, comprising evaluating expression of one or more of the predictive biomarkers as defined in Table 1 above.

In particular, said predictive biomarkers which can be used in the monitoring methods of the invention are selected from the group consisting of epidermal growth factor (EGF), monocyte chemoattractant protein 1 (MCP1), and transforming growth factor alpha (TGF-α), and, optionally, NGAL.

In such monitoring methods, the evaluating step is carried out essentially the same as decribed above for the prediction method of the invention, except that the subject is a subject in need of a treatment for CKD, and the predictive biomarkers are now used as surrogate markers, i.e. as predictors of the disease stage or of mGFR parameter. The evaluating step can be performed for example prior to treatment, and during or at the end of the treatment and evolution/change of expression levels of each biomarker is indicative of a good or poor response to the treatment.

Such methods can be used for example to monitor the efficacy of regulatory approved treatments of CKD in patients, and/or adjust dosage or length of the treatment. Alternatively, such methods can be used to monitor the efficacy of candidate treatments in clinical studies.

Kits of the Invention

The invention further relates to kits for the prediction or monitoring methods of the invention.

In particular, one object of the invention consists of a kit for the prediction of fast progression of chronic kidney disease in a subject (e.g. in a biological sample, more particularly in a urine sample of the subject), said kit comprising means for quantifying protein expression of at least the following biomarkers: epidermal growth factor (EGF), monocyte chemoattractant protein 1 (MCP1), and transforming growth factor alpha (TGF-α).

Another object of the present invention consists of a kit for monitoring the efficacy of a treatment for treating chronic kidney disease, said monitoring kit comprising means for quantifying protein expression of at least the following predictive biomarkers: epidermal growth factor (EGF), monocyte chemoattractant protein 1 (MCP1), and transforming growth factor alpha (TGF-α).

In certain embodiments of said kits, they comprise means for detecting and/or quantifying 2, 3, or all of the following predictive biomarkers: EGF, MCP1, TGF-α and NGAL.

The monitoring or prediction kit of the invention may thus include a plurality of reagents, each of which is capable of binding specifically with a protein of one of the predictive biomarkers. Suitable reagents for binding specifically with a protein biomarker include, without limitation, antibodies.

As used herein the term “antibody” is used in a broader sense and includes whole antibodies and any antigen binding fragments or derivatives (i.e., “antigen-binding portion”). It may specifically cover monoclonal antibodies, polyclonal antibodies, multi-specific antibodies, single chains thereof. Antigen binding fragments also include, Fab, Fab′, (Fab′)2 and their derivatives including a combination of VH and VL fragments, or Fv antibodies, Fv being the minimum antibody fragment which contains a complete antigen-recognition and—binding site. It further includes, chimeric, humanized or human antibodies.

A naturally occurring “antibody” is a glycoprotein comprising at least two heavy (H) chains and two light (L) chains inter-connected by disulfide bonds. Each heavy chain is comprised of a heavy chain variable region (abbreviated herein as VH) and a heavy chain constant region. The heavy chain constant region is comprised of three domains, CH1, CH2 and CH3. Each light chain is comprised of a light chain variable region (abbreviated herein as VL) and a light chain constant region. The light chain constant region is comprised of one domain, CL. The VH and VL regions can be further subdivided into regions of hypervariability, termed complementarity determining regions (CDR), interspersed with regions that are more conserved, termed framework regions (FR). Each VH and VL is composed of three CDRs and four FRs arranged from amino-terminus to carboxy-terminus in the following order: FR1, CDR1, FR2, CDR2, FR3, CDR3, FR4. The variable regions of the heavy and light chains contain a binding domain that interacts with an antigen.

An “isolated antibody”, as used herein, refers to an antibody that is substantially free of other antibodies having different antigenic specificities. Moreover, an isolated antibody may be substantially free of other cellular material and/or chemicals.

The terms “monoclonal antibody” and “monoclonal antibody composition” as used herein refer to a preparation of antibody molecules of single molecular composition. A monoclonal antibody composition displays a single binding specificity and affinity for a particular epitope.

The term “recombinant human antibody”, as used herein, includes all human antibodies that are prepared, expressed, created or isolated by recombinant means, such as antibodies isolated from an animal (e.g., a mouse) that is transgenic or transchromosomal for human immunoglobulin genes or a hybridoma prepared therefrom, antibodies isolated from a host cell transformed to express the human antibody, e.g., from a transfectoma, antibodies isolated from a recombinant, combinatorial human antibody library, and antibodies prepared, expressed, created or isolated by any other means that involve splicing of all or a portion of a human immunoglobulin gene, sequences to other DNA sequences. Such recombinant human antibodies have variable regions in which the framework and CDR regions are derived from human germline immunoglobulin sequences. In certain embodiments, however, such recombinant human antibodies can be subjected to in vitro mutagenesis (or, when an animal transgenic for human Ig sequences is used, in vivo somatic mutagenesis) and thus the amino acid sequences of the VH and VL regions of the recombinant antibodies are sequences that, while derived from and related to human germline VH and VL sequences, may not naturally exist within the human antibody germline repertoire in vivo.

As used herein, “isotype” refers to the antibody class (e.g., IgM, IgE, IgG such as IgG1 or IgG4) that is provided by the heavy chain constant region genes.

The phrases “an antibody recognizing an antigen” and “an antibody specific for an antigen” and “an antibody reacting specifically to an antigen” are used interchangeably herein with the term “an antibody which binds specifically to an antigen”.

As used herein, an antibody or a protein that “specifically binds to a protein biomarker” is intended to refer to an antibody or protein that binds to said protein biomarker with a KD of 100 nM or less, 10 nM or less, 1 nM or less, 100 pM or less, or 10 pM or less.

The term “Kassoc” or “Ka”, as used herein, is intended to refer to the association rate of a particular antibody-antigen interaction, whereas the term “Kdis” or “Kd,” as used herein, is intended to refer to the dissociation rate of a particular antibody-antigen interaction.

The term “KD”, as used herein, is intended to refer to the dissociation constant, which is obtained from the ratio of Kd to Ka (i.e. Kd/Ka) and is expressed as a molar concentration (M). KD values for antibodies can be determined using methods well established in the art. A method for determining the KD of an antibody is by using surface plasmon resonance, or using a biosensor system such as a Biacore® system.

The prediction or monitoring kit of the invention may further include antibodies for detecting or quantifying capture antibodies in an immunoassay.

For example, the monitoring or prediction kit of the invention may include second antibodies for detecting or quantifying biomarker protein/antibodies complex in an immunoassay. Accordingly, the monitoring or prediction kit comprises

(i) a set of unlabeled antibodies which, each, bind to one or more predictive biomarkers particularly selected from the group consisting of: epidermal growth factor (EGF), monocyte chemoattractant protein 1 (MCP1), and transforming growth factor alpha (TGF-α), and/or (ii) a set of labelled antibodies which, each, bind to one or more predictive biomarkers particularly selected from the group consisting of: epidermal growth factor (EGF), monocyte chemoattractant protein 1 (MCP1), and transforming growth factor alpha (TGF-α),

Alternatively, the kit may comprise:

(i) a first set of unlabeled antibodies which, each, bind to one or more predictive biomarkers particularly selected from the group consisting of: epidermal growth factor (EGF), monocyte chemoattractant protein 1 (MCP1), and transforming growth factor alpha (TGF-α), (ii) a second set of labelled antibodies which bind to said first set of labelled antibodies to form a biomarker/first antibody/second antibody complex.

The labelled antibody as used in the kits of the invention may typically comprise an antibody conjugated to a reporter molecule. In specific embodiments of the kits as above defined, the labelled antibody is an antibody conjugated to an enzyme, for example an enzyme for ELISA assay, such as horseradish peroxidase, glucose oxidase, -galactosidase, and alkaline phosphatase, amongst others. The kit may further include corresponding substrates for such enzymes.

Alternatively, the labelled antibody is an antibody conjugated with fluorescent compounds, for example, fluorescin or rhodamine.

The kits as defined above may further include antibodies (unlabelled and/or labelled) which bind to NGAL.

Optionally, said monitoring or prediction kit of the invention may further comprise means for detecting and/or quantifying one or more reference (control) marker, e.g. markers corresponding to ubiquitously expressed proteins, or other biomarker of CKD, such as creatinine or albumin.

In a specific embodiment, the kit of the invention, for use in an immunoassay, comprises at least:

(i) antibodies specific of epidermal growth factor (EGF); (ii) antibodies specific of monocyte chemoattractant protein 1 (MCP1); and, (iii) antibodies specific of transforming growth factor alpha (TGF-α).

Said antibodies for use in the kits of the invention may be immobilized on a support, for example a glass or a polymer, more particularly, polymers selected among cellulose, polyacrylamide, nylon, polystyrene, polyvinyl chloride or polypropylene. The solid supports may be in the form of tubes, beads, discs of microplates, or any other surface suitable for conducting an immunoassay.

The prediction kit and the monitoring kit of the invention may optionally comprise additional components useful for performing the methods of the invention. By way of example, the kits may comprise fluids (e.g. buffers) suitable for binding an antibody with a protein with which it specifically binds, one or more sample compartments, washing the reactants to remove unspecific binding, and instructional materials which describe performance of the prediction or monitoring methods of the invention and the like.

Another aspect of the invention provides for a device adapted for carrying out the prediction or monitoring methods of the invention, said device comprising:

a) an analyzing unit comprising a combination of detection means which specifically bind to the biomarkers (e.g. antibodies as described above in the kits), the analyzing unit being adapted for contacting, in vitro, the sample from the subject with the detection agent; b) an evaluation unit including a computing device having a database and a computer-implemented algorithm on the database, the computer-implemented algorithm when executed by the computing device determines an amount of one or more of the predictive biomarkers in the sample, e.g., urine sample, from the subject, and compares the determined amount of said one or more predictive biomarker with corresponding control values and provides a prediction of fast progression if the amount of said one or more predictive biomarker is significantly different, for example significantly greater or lower than a control value.

In one specific embodiment, the database further includes the control values for each of the predictive biomarkers.

Examples Methods NephroTest Cohort Protocol

Patients were recruited from the NephroTest cohort that includes 1825 patients with mGFRs with plasma and/or urine samples to date since its commencement in 2000. The NephroTest cohort consists of patients from three large nephrology centres in Paris: Hôpital Européen Georges Pompidou, Hôpital Bichat and Hôpital Tenon. Inclusion criteria to the cohort were consenting adult patients with any stage of CKD. Exclusion criteria were patients already on any form of renal replacement therapy and pregnant patients. The urine collection biobank in the protocol commenced in 2009. We analysed data from 229 patients in whom there were stored urine collections and 2 or more concurrent mGFRs over time in order to assess disease progression. The median follow-up time for these patients in the cohort from the time of the first urine collection was 21.6 (IQR, 13.6-24.7) months, and 57 of these patients had up to 3 sequential mGFRs from time of first urine collection. First urine collection from these 229 patients was analysed. The clinical and biological data collected from patients is as previously described

The NephroTest cohort study is approved and sponsored by the French Institute of Health and Medical Research (INSERM). All patients provided written informed consent for long-term handling of frozen biological samples and of pre-specified clinical and biologic data for research use. The study was conducted in accordance with good clinical practice guidelines.

Technical Methods (Urinary Biomarkers Measurements)

Urine was collected and stored at −80° C. after a maximal 4 hour period at 4° C. Urine supernatant was stored after initial centrifugation at 1000 g for 10 minutes in a tube containing antiprotease (cOmplete; Protease Inhibitor Cocktail Tablets, Roche applied Science).

The biomarkers ELISA kits used were R&D systems for cystatin C, epidermal growth factor (EGF), growth and differentiation factor 15 (GDF15), interleukin 6 (IL6), monocyte chemoattractant protein 1 (MCP1), matrix metalloprotease 9 (MMP9), osteopontin, kidney injury molecule 1 (KIM1), tissue inhibitor of metalloprotease 1 (TIMP1), Transforming growth factor alpha (TGF-α), and vascular endothelial growth factor A (VEGFA); eBioscience for Fibronectin and leukaemia inhibitory factor (LIF); Bioporto for neutrophil gelatinase associated lipocain (NGAL); CMIc for fatty acid binding protein 1 (FABP1), and MdBiproduct for uromodulin. All ELISA kits were formally validated following rigorous criteria prior to utilisation [unpublished data].

Urine creatinine was measured in the biochemistry laboratory at Hôpital Necker Enfants-Malades using the enzymatic method standardised for isotope dilution mass spectrometry (IDMS). Urinary protein and albumin were measured the standard methods in the hospital laboratory.

Measurement and Definition of CKD Progression

As reported elsewhere, all patients in NephroTest cohort had GFRs measured by chromium-51 labelled ethylenediaminetetraacetic (EDTA) acid clearance.³¹ Individual slopes in mL/min/year were estimated using ordinary linear regression. We then calculated relative mGFR slopes in % per year and categorized patients into two groups of rate of decline in baseline mGFR: ≤10 vs.>10% per year. Estimated GFRs (eGFRs) were also calculated using the Modification of the in Renal Disease formula³² and then the values obtained were unadjusted for body surface area (BSA) to allow for a direct comparison of the eGFR with the corresponding mGFR values.

Statistical Analysis

Clinical and laboratory data are expressed as percentages, means (±standard deviation, SD) or median (interquartile range, IQR), as appropriate. Biomarkers, albuminuria and proteinuria to creatinine ratios, have skewed distributions and thus were subsequently logarithmically transformed. NGAL and cystatin C required two sequential logarithmic transformations to approach a normal distribution. Log-transformed values were then standardized to a mean of 0 and SD of 1, using gender specific means and SDs i.e. (measured value−mean) divided by the SD, to account for gender-related differences.

We compared baseline clinical and laboratory data and CKD risk factors between slow and fast progressors (using the cut off of 10% decline per year in mGFR). Continuous variables were compared with the Wilcoxon test and categorical variables with the chi-squared or Fisher's exact test. Gender differences regarding biomarkers and mGFR slopes were similarly tested. Association between biomarkers and concurrently mGFR at first visit and between biomarkers and % change in mGFR were first assessed using Pearsons correlation. Logistic regression was then used to estimate the crude and adjusted odds ratios (OR) of fast progression for each of the biomarkers. OR were sequentially adjusted for mean mGFR over time and baseline covariates: age, gender, black African origin, BMI, mean blood pressure, diabetes mellitus, history of cardiovascular disease, smoking, renin angiotensin aldosterone system (RAAS) blockade such as angiotensin converting enzyme inhibitor (ACEI) or angiotensin receptor blocker (ARB) treatment, and finally for albuminuria. The mean mGFR instead of the baseline mGFR was used as a covariate in the analysis to reduce the phenomenon of ‘regression to the mean’³.

In order to determine the best combination of biomarkers to predict CKD progression, several models of logistic regression combining biomarkers and albuminuria were built a priori principally from pathophysiological hypotheses. Biomarkers associated individually with CKD progression were favoured in the model but we avoided adding together biomarkers correlating with each other or with albuminuria at more than 0.55 (Pearson correlation). These models were additionally adjusted for mean GFR and other significantly associated factors such as centre of recruitment and diabetes to limit the number of covariates. The six models constructed as such, were compared to a nested minimal model with only albuminuria and the same adjustment covariates but without the biomarkers. We calculated the area under the receiver operating characteristic (ROC) curve (AUC), the likelihood ratio test (LRT), the scale brier score (SBS), and the integrated discrimination index (IDI) to assess performance of theses predictive models for CKD progression. The improvement in the predictive value was estimated by comparing AUC of these models with the minimal reference model.

Statistical analyses were performed with SAS 9.2 (SAS Institute Inc., Cary, N.C. USA), R 2.3 (R Foundation for Statistical Computing, Vienna, Austria, 2014) and prism graphpad 5.0 for the animal data.

Results Patient Characterisation and CKD Progression

We analysed in 229 patients with repeated mGFRs and concurrent urine collections. There were 152 (66.4%) men and the mean age at baseline (i.e. at the time at which first urine collection was performed) was 60.8±13.3 years. The median baseline mGFR was 38.3 (IQR, 26.4-49.6) ml/min. The median mGFR at last visit after a median follow up time of 21.6 (IQR, 13.6-24.7) months was 34.7 (IQR, 24.7-47.5) ml/min. The change in mGFR was −1.46 (IQR, −4.28, 1.08) ml/min/year equivalent to −4.0 (IQR, −12, 2.7) % per year from baseline (negative values represent a loss). 68 (30%) patients were defined as ‘fast progressors’ (loss of greater than 10% in their mGFR per year). The baseline characteristics in the slow and fast progressors are shown in Table 2. Albuminuria was significantly higher in fast progressors and diabetes was more frequent. Although a majority of the patients (89%) were on ACEI or ARB treatment, a higher proportion of the fast-progressors were on treatment (96% vs. 86%, p<0.04). Median mGFR during follow-up was as expected, lower in fast than in slow progressors (median IQR: 29.3(20.0, 37.5) vs. 40.9(30.1, 52.3) ml/min, p<0.0001), and the baseline mGFR was slightly lower in the fast progressors (p=0.06). Importantly, the number of mGFR measurements used to estimate progression rate was not significantly different between the two groups (more than 2 visits for 47% vs. 38%, p=0.2).

TABLE 2 Baseline characteristics of NephroTest patients. Slow Fast p- All progressors Progressors value N 229 161 68 Age, years 60.8 ± 13.3 60.3 ± 13.3 62.0 ± 13.4 0.4 Men 66% (152) 66% (106) 68% (46) 0.8 Black African 10% (23) 12% (19) 6% (4) 0.2 Diabetes 25% (57) 21% (34) 34% (23) 0.04 Follow up (months) 21.5 (13.4, 24.6) 23.0 (14.3, 24.9) 17.6 (12.8, 24.1) 0.07 First mGFR (mls/min) 38.3 (26.3, 49.6) 39.9 (28.9, 50.4) 34.9 (24.4, 44.0) 0.06 Mean Blood Pressure 92 ± 13 92 ± 12 92 ± 14 0.9 Elevated blood pressure 26% (57) 24% (38) 28% (19) 0.5 (>140/90) ACEI or ARB 89% (202) 86% (137) 96% (65) 0.04 Body Mass Index, kg/m² 27.2 ± 5.9  27.0 ± 5.8  27.7 ± 6.1  0.3 History of Cardiovascular 17% (38) 16% (25) 19% (13) 0.5 Event Previous/Current smokers 38% (87)/10% (22) 35% (57)/11% (17) 44% (30)/7% (5) 0.4 Type of Nephropathy PKD 7% (16) 4% (7) 13% (9) 0.2 Diabetic nephropathy 8% (19) 7% (12) 10% (7) Glomerular disease 16% (37) 16% (26) 16% (11) Vascular 25% (57) 26% (42) 22% (15) Chronic Interstitial 17% (39) 19% (30) 13% (9) Disease Unknown 27% (61) 27% (44) 25% (17) Proteinuria (mg/mmol) 0.2 (0.1, 0.5) 0.1 (0.1, 0.4) 0.3 (0.1, 0.7) 0.0002 Albuminuria (mg/mmol) 77 (17,291) 52 (12,228) 189 (40,485) 0.0001

ACEI: Angiotensin converting enzyme inhibitor; ARB: angiotensin receptor blocker; mGFR: measured glomerular filtration rate by chromium labelled EDTA; PKD: polycystic kidney disease.

Data are expressed as mean±standard deviation (SD), median (interquartile range (IQR)) or percent % (number of patients (n))

Biomarker Characterization and Gender Distribution

In biomarkers, TIMP1, VEGFA, KIM1, and osteopontin, where only less than 5% of the patients had values falling below the lower limit of detection (LLD), half of the value for the LLD was imputed for each patient (no more than 5 values were imputed in each case). In biomarkers (proteinuria, albuminuria, and FABP1) were 5 to 10% of the patients were affected, a random value between 0 and 1 multiplied by the LLD value was imputed. Finally, the biomarkers MMP9, IL6, and LIF, where more than 20% of the patients had values below the LLD, were described but not included in our subsequent analysis.

The biomarker distributions in the cohort are detailed in Table 3. The distributions varied by gender in five of the biomarkers (MMP9 and LIF not included). We found at baseline significantly higher levels of NGAL, TGF-α, and uromodulin in women. Conversely, we also observed higher levels of TIMP1 and VEGFA in men. There was no difference in baseline mGFR normalised for body surface area (BSA) between men and women (36.8 vs. 34.6 mls/min/1.73 m², p=0.3). The proportion of ‘fast progressor’ men was not significantly different from the ‘slow progressor’ men (68% vs. 66%, p=0.8, Table 1).

TABLE 3 Distribution of urinary biomarkers by gender All Men Women N median [IQR] median [IQR] median [IQR] P-value Cystatin C, ng/mmol 229 4.99 [3.28-17.22] 4.94 [3.17-17.71] 5.31 [3.45-14.2] 0.5 EGF, μg/mmol 229 0.60 [0.38-0.93] 0.56 [0.37-0.86] 0.64 [0.40-1.18] 0.06 FABP1, μg/mmol 216 1.58 [0.59-5.12] 1.79 [0.58-5.57] 1.43 [0.66-3.75] 0.6 Fibronectin, 229 61.0 [44.7-92.3] 59.5 [42.4-91.8] 65.7 [48.5-92.2] 0.2 μg/mmol GDF15, μg/mmol 229 0.75 [0.49-1.16] 0.79 [0.53-1.19] 0.62 [0.44-1.12] 0.09 IL6, ng/mmol 176 0.43 [0.17-0.82] 0.39 [0.16-0.84] 0.48 [0.17-0.81] 0.5 KIM1, μg/mmol 222 0.07 [0.04-0.12] 0.07 [0.04-0.12] 0.07 [0.04-0.12] 0.5 LIF, ng/mmol 154 2.64 [1.62-4.55] 2.41 [1.48-3.82] 3.47 [2.01-5.47] 0.02 MCP1, ng/mmol 229 24.0 [15.5-39.4] 22.3 [15.3-40.1] 26.3 [15.9-38.1] 0.5 MMP9, μg/mmol 168 0.05 [0.01-0.26] 0.03 [0.01-0.08] 0.26 [0.05-0.90] <0.0001 NGAL, μg/mmol 229 4.12 [2.19-10.81] 2.98 [1.79-7.12] 8.00 [3.28-18.34] <0.0001 Osteopontin, 227 76.0 [45.8-107.1] 74.7 [46.0-106] 77.1 [36.7-109] 0.99 μg/mmol TIMP1, μg/mmol 228 0.24 [0.13-0.47] 0.31 [0.18-0.56] 0.14 [0.08-0.25] <0.0001 TGF-α, ng/mmol 229 0.38 [0.26-0.54] 0.33 [0.24-0.48] 0.44 [0.34-0.55] 0.0003 Uromodulin, 227 1.58 [0.81-2.97] 1.39 [0.74-2.91] 2.09 [1.21-2.98] 0.02 mg/mmol VEGFA, ng/mmol 227 10.2 [6.1-16.5] 11.5 [6.8-19.4] 8.7 [4.3-11.2] <0.0001

Values given are adjusted for urine creatinine concentration.

N, number of patients; IQR, Interquartile range; min: minimum value recorded; max: maximum value recorded; EGF, Epidermal Growth Factor; FABP1, Fatty Acid Binding Protein 1; GDF15, Growth Differentiation Factor 15; IL6, Interleukin 6; KIM 1, Kidney Injury Molecule 1; LIF, Leukemia Inhibitory Factor; MMP9, Matrix Metalloprotease 9; MCP1, Monocyte Chemoattractant Protein 1; NGAL, Neutrophil Gelatinase Associated Lipocalin; TIMP 1, Tissue Inhibitor of Metalloprotease 1; TGF-α, Transforming Growth Factor alpha; VEGFA Vascular Endothelial Growth Factor A.

Individual Biomarker Association with CKD Progression

We observed no correlation between the baseline mGFR and the percent change in progression (r=−0.06, p=0.4). We then studied the association between urinary protein levels and the risk of being a ‘fast progressor’. We initially studied each of the biomarkers individually and then subsequently sequentially adjusted each biomarker for known important risk factors for CKD progression including albuminuria in a multivariable logistic regression analysis (Table 4). The risk of being in a ‘fast progressor’ status was, as expected, increased by albuminuria and/or proteinuria. It was also increased by elevations in levels of cystatin C, FABP1, fibronectin, GDF15, KIM1, MCP1, NGAL, TIMP1, TGF-α and VEGFA. The risk was however reduced by increases in EGF. When traditional risk factors for CKD progression were added to the regression model, the risks were attenuated and there was further attenuation by adjusting for albuminuria (Table 4). Fibronectin, MCP1, TIMP1 and TGF-α however remained significantly associated with the risk after taking into account the role of all these factors. Interestingly, the effect size on progression was higher with TGF-α (OR 2.34) than with albuminuria (OR 1.72).

TABLE 4 biomarker correlation with risk of ‘fast progressor’ (fall in mGFR >10% per year) status. +mean mGFR Biomarker +*covariates +albumin OR (95% CI) OR (95% CI) OR (95% CI) Protein 2.15 (1.52, 3.03) 1.87 (1.24, 2.83) Albumin 2.08 (1.48, 2.91) 1.72 (1.14, 2.58) Cystatin C 1.92 (1.40, 2.65) 1.72 (1.13, 2.60) 1.44 (0.91, 2.28) EGF 0.62 (0.46, 0.84) 0.87 (0.54, 1.38) 0.96 (0.59, 1.56) FABP 1.87 (1.37, 2.54) 1.53 (1.06, 2.22) 1.29 (0.85, 1.96) Fibronectin 1.59 (1.18, 2.15) 1.60 (1.13, 2.25) 1.48 (1.04, 2.11) GDF15 1.60 (1.17, 2.19) 1.53 (1.05, 2.22) 1.40 (0.95, 2.04) KIM1 1.70 (1.16, 2.51) 1.54 (1.01, 2.33) 1.35 (0.89, 2.06) MCP1 2.10 (1.51, 2.93) 2.09 (1.45, 3.01) 1.95 (1.35, 2.82) NGAL 1.50 (1.12, 2.00) 1.38 (0.98, 1.95) 1.25 (0.87, 1.79) Osteopontin 1.09 (0.80, 1.48) 1.14 (0.80, 1.62) 1.10 (0.79, 1.54) TIMP1 2.02 (1.45, 2.83) 1.91 (1.31, 2.78) 1.72 (1.15, 2.55) TGF-α 2.08 (1.48, 2.92) 2.34 (1.57, 3.48) 2.36 (1.57, 3.54) Uromodulin 1.26 (0.94, 1.69) 1.29 (0.91, 1.83) 1.29 (0.90, 1.84) VEGFA 1.82 (1.31, 2.53) 1.62 (1.08, 2.41) 1.46 (0.97, 2.20) Abbreviations: mGFR, measured glomerular filtration rate; OR, odds ratio; CI, confidence interval; EGF, Epidermal Growth Factor; FABP1, Fatty Acid Binding Protein; GDF15, Growth and Differentiation Factor 15; KIM1, Kidney Injury Molecule 1; MCP1, Monocyte Chemoattractant Protein 1; NGAL, Neutrophil Gelatinase Associated Lipocalin; TIMP1, Tissue Inhibitor of Metalloprotease 1, TGF-α, Transforming Growth Factor alpha, VEGFA, Vascular Endothelial Growth Factor A.

OR presented are per gender-specific standard deviation (SD) unit increase (after log-transformation). *covariates: age, body mass index, mean arterial pressure, gender, Black/African ethnicity, diabetes, history of cardiovascular disease, smoker, angiotensin converting enzyme inhibitor or angiotensin receptor blocker, and recruitment centre

Combining Urinary Biomarkers to Predict CKD Progression

Six different models combining 4 to 6 biomarkers were compared against the reference model with albuminuria (Table 5).

TABLE 5 Biomarker combinations giving 6 different models associated with risk of fast progression: *odds ratio (95% CI) of ‘fast progressor’ status (mGFR decline > 10% per year)· Model 0 Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Albumin 1.8  1.7  1.76 1.55 1.49 1.68 1.6  (1.23-2.62) (1.06-2.72) (1.08-2.86) (1.00-2.41) (0.96-2.30) (1.07-2.65) (1.03-2.49) EGF 0.54 0.55 0.59 0.59 0.51 0.67 (0.32-0.94) (0.33-0.95) (0.35-0.99) (0.35-1.01) (0.29-0.88) (0.40-1.10) GDF15 0.94 0.94 0.85 0.82 0.99 1.00 (0.59-1.48) (0.60-1.48) (0.56-1.30) (0.54-1.24) (0.63-1.54) (0.67-1.49) TGF-α 2.53 2.53 2.4  2.39 2.9  2.15 (1.58-4.04) (1.59-4.03) (1.52-3.80) (1.53-3.74) (1.76-4.75) (1.39-3.32) MCP1 1.96 2.07 1.99 1.83 2.22 (1.31-2.94) (1.36-3.14) (1.32-2.99) (1.19-2.81) (1.45-3.40) FABP1 0.73 (0.43-1.24) Cystatin C 0.68 (0.39-1.20) Fibronectin 0.91 (0.58-1.38) KIM1 1.20 (0.77-1.86) NGAL 0.54 (0.32-0.91) TIMP1 0.98 1.18 (0.95-1.01) (0.74-1.90) LRT (df) Ref 33.9  34.6  32.8  33.3  38.3  21.6  (5) (6) (5) (5) (5) (4) p-value <0.0001 <0.0001 <0.0001 <0.0001 <0.0001  0.0002 AUC  0.769 0.838 0.842 0.838 0.839 0.849 0.807 SBS 0.18 0.31  0.32  0.31  0.31  0.33  0.26  IDI/rIDI Ref 13.3/74.1 13.5/75.5 12.8/71.7 12.8/71.8 15.2/84.2 8.1/45.5 Abbreviations: CI, confindence interval; SD, standard deviation; EGF, Epidermal Growth Factor; GDF15, Growth and Differentiation Factor 15; TGF-α, Transforming Growth Factor alpha; MCP1, Monocyte Chemoattractant Protein 1; FABP1, Fatty Acid Binding Protein; KIM1, Kidney Injury Molecule 1; NGAL, Neutrophil Gelatinase Associated Lipocalin; TIMP1, Tissue Inhibitor of Metalloprotease 1; LRT, likelihood ratio test; df, degrees of freedom; Ref, reference model; AUC, area under the curve; SBS, scale Brier score; IDI, integrated discrimination index; rIDI, relative IDI. *odds ratios presented are per gender-specific SD unit increase (after log-transformation). Models were adjusted for mean mGFR across visits, diabetes and recruitment centre.

Models were based on having biomarker reflecting different pathophysiological process and interbiomaker correlation within each model of <0.55. These models were adjusted only for mean mGFR and for significantly associated factors i.e. diabetes and the (NephroTest) recruitment centre. All models with biomarkers performed better than the reference model without the biomarkers (Table 5, likelihood ratio test), but the model containing EGF, GDF15, MCP1, NGAL, and TGF-α (model 5) was the best model according to all the performance criteria employed. In particular, biomarkers in model 5 significantly increased the area under the curve (AUC) of the receiver operating characteristic (ROC) curve from 0.77 to 0.85 (p=0.006) from the reference model (with albumin and without other biomarkers). Supporting these results, we also carried out a step-wise regression analysis with no a priori hypothesis as to functional role of biomarkers or interbiomarker correlations and interestingly obtained exactly the same combination of biomarkers.

Because GDF15 was not found to be associated with progression in the model (OR 0.99 (0.63-1.54)), we tested and observed that when removed from the model, performance criteria of this new model were unaffected (data not shown). Surprisingly, we also observed that NGAL similar to EGF in our model appeared to confer a protective effect (Table 5). This appeared contrary to its individual effect on disease progression where it was associated with an increased risk of being a ‘fast progressor’. Analysing NGAL in tertiles instead of continuously revealed its complex relationship with progression (J-shaped pattern) after adjustment for other biomarkers (FIG. 3). For these reasons, model 5 could be simplified by removing GDF15 and NGAL and we provide a final model predicting fast progression with EGF, MCP1 and TGF-α in addition to albuminuria with a c-statistic of 0.84 still superior to that of albumin and the other risk factors (see FIG. 1).

Comparing mGFR and eGFR

Using eGFR instead of mGFR to define CKD progression in our final model gave us different results. Only TGF-α and albumin remained significantly associated with progression in simplified model 5, OR (95% CI) of 1.51(1.07-2.12) and 1.55(1.05-2.27)) respectively but neither EGF nor MCP1, 0.72(0.45-1.15) and 1.21(0.86-1.69) respectively. As expected, correlation between eGFR and mGFR was high (r=0.87) and GFR was overestimated with the formula compared to the gold standard (median bias IQR 2.60 (1.46, 3.76)). Proportion of fast progressors (GFR decline >10%/year) remained the same (29.69%) using either mGFR or eGFR. However, we observed that of the 68 ‘fast progressors’ in the cohort defined by mGFR only 38 (56%) were correctly classified as being ‘fast progressors’ by using eGFRs giving eGFR quite a low sensitivity for identifying ‘fast progressors’ (FIG. 2).

DISCUSSION

We did not find an improvement in predictability in our cohort using this model since our outcome measures were different. It would be interesting however to see if the prediction could be ameliorated with the addition of this new biomarker signature.

It is acknowledge that RAAS blockade slows down CKD progression. The observation here that more of the fast-progressors were on ACEI and ARB drugs most likely reflects the clinicians' response to the higher levels of albuminuria in the ‘fast progressor’ group than a contrary effect of the RAAS blockade in this group42.

This is particularly evident from our results comparing the two techniques as we show here demonstrating the much reduced sensitivity of the eGFR in detecting disease progression. In the context of a discovery observational study such as this one, the well-characterised patient cohort with rigorously mGFRs has been very useful. Having very accurate mGFRs here enabled us correctly classify our patients in their true progressor statuses and as a consequence we could correlate this accurately with the variations in biomarkers in a relatively small cohort and still draw valid significant findings which have not been possible with the less accurate eGFR in which greater numbers of patients would be required.

Since the progress of CKD is a complex pathophysiological process involving many pathways, the need for a combination of several biomarkers (as opposed to a unique biomarker) may be required to make accurate predictions. Although this cohort is relatively small cohort and most of the patients had only two mGFRs over the follow-up period, it has several advantages. The use of the mGFR has already been discussed but in addition this was also an ethnically diverse cohort and contained a good representation of the various pathologies, which account for CKD in registries. We managed to narrow down the number of biomarkers in our final model owing to a degree of redundancy.

In conclusion, we propose TGF-α, EGF, and MCP1 together with albumin and demographic risk factors as a new molecular signature of CKD progression to be potentially used early in the disease process.

REFERENCES

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1. An in vitro method for the prediction of fast progression of chronic kidney disease in a subject, comprising the steps of evaluating the expression of one or more biomarkers in a biological sample obtained from said subject, wherein said one or more biomarkers are selected from the group consisting of transforming growth factor alpha (TGF-α), epidermal growth factor (EGF), and monocyte chemoattractant protein 1 (MCP1).
 2. The method of claim 1, comprising evaluating at least the expressions of EGF, MCP1, and TGF-α, and, optionally, Neutrophil Gelatinase-associated Lipocalin (NGAL).
 3. The method of claim 2, wherein the evaluating step includes (a) quantifying the expression of one or more of the selected biomarkers in a biological sample obtained from said subject to obtain an expression value for each quantified biomarker, and (b) comparing said expression value obtained at step (a) to a corresponding control value, wherein an expression value of TGF-α above a control value, and/or an expression value of EGF below a control value and/or an expression value of MCP1 above a control value and/or an expression value of NGAL above a control value, indicates that the subject is at increased risk of fast progression.
 4. The method of claim 3, wherein the expression of one or more biomarkers selected from the group consisting of: Growth and Differentiation Factor 15 (GDF15), Neutrophil Gelatinase-associated Lipocalin (NGAL), Cystatin C, Fatty Acid Binding Protein (FABP), Fibronectin, Kidney Injury Molecule 1 (KIM1), Osteopontin, Tissue Inhibitor of Mettaloprotease 1 (TIMP1), Uromodulin, Interleukin-6 (IL6), Leukemia Inhibitory Factor (LIF), Matrix Metallopeptidase 9 (MMP9) and Vascular Endothelial Growth Factor A (VEGFA) is further evaluated.
 5. The method of claim 4, wherein said biological sample is urine or serum sample.
 6. The method of claim 5, wherein said expression is protein expression, for example as quantified in an immunoassay.
 7. The method of claim 6, wherein a subject predicted of fast progression is a subject who is at risk of losing more than 10% of the baseline measured glomerular filtration rate (mGFR) per year.
 8. The method of claim 7, wherein said subject predicted of fast progression is then selected for treatment with a therapeutic agent for treating chronic kidney disease.
 9. An in vitro method for monitoring the efficacy of a therapeutic agent for treating chronic kidney disease in a subject, comprising evaluating the expressions of biomarkers in a biological sample of said subject, wherein said biomarkers are TGF-α, EGF, and, MCP1, and, optionally, evaluating the expression of NGAL.
 10. The method of claim 9, comprising a first evaluating step prior to treating chronic kidney disease and repeating said evaluating step during or after said treatment step, wherein a change in the expressions of said biomarkers is indicative of a response to said treatment.
 11. A kit for carrying out the method of claim 3, said kit comprising means for quantifying protein expression of at least the following biomarker: TGF-α, EGF, and MCP1, and, optionally, means for quantifying protein expression of NGAL.
 12. The kit of claim 11, wherein said means for quantifying protein expression include unlabelled antibodies specific of each biomarker and, optionaly, second labeled antibodies for detecting said biomarker/unlabelled antibodies in an immunoassay.
 13. The kit of claim 12, for use in an immunoassay, comprising: antibodies specific of TGF-α, antibodies specific of EGF; and, antibodies specific of MCP1; optionally antibodies specific of NGAL.
 14. The kit of claim 13, wherein said antibodies are immobilized on a support.
 15. The kit of claim 14, wherein said antibodies are conjugated to reporter molecule. 